fgeo 1.0.0

fgeo — Generative Engine Optimization CLI. AI-powered content asset management and multi-platform distribution.

fgeo

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fgeo is not just a software system.

It is a learning methodology, packaged as an out-of-the-box agent skill, that helps you turn real work into growth.

fgeo is built around one formula:

User Growth = Action x Feedback x Adaptation

You grow when you do something real, receive meaningful feedback, and change the next attempt.

What fgeo Is

fgeo is a local-first growth companion for AI-assisted work.

It helps an agent understand your goal, route the work to the right skill, and keep the learning loop alive:

Action -> Feedback -> Adaptation

The CLI provides the local state boundary. The agent skills provide the methodology and workflow guidance.

Together, they help you:

• understand your current level and what is worth improving next,
• practice through small real attempts,
• turn learning into reusable records, cases, and content,
• publish content and route real-world signals back into learning,
• avoid starting from zero every time you open a new agent session.

Why the Formula Matters

The formula uses multiplication because any missing part breaks the loop:

• Action without Feedback becomes blind repetition.
• Feedback without Action becomes analysis without experience.
• Action plus Feedback without Adaptation becomes awareness without growth.

Time and memory are not the formula itself. Time only matters when useful loops repeat. Memory helps fgeo preserve prior signals, reuse context, and time the next loop so each session does not start from zero.

fgeo Agent Skills

fgeo routes user intent into one primary workflow at a time:

need clarity / current level / review / feedback meaning -> fgeo-digest
need practice / improvement / mastery / next attempt     -> fgeo-growth
need expression / draft / story / case / article         -> fgeo-content
need publish / external signal / metrics / reply         -> fgeo-publish
need identity / audience / preference / boundary         -> fgeo-profile

Each skill supports a different part of the learning loop:

Skill	Role
fgeo-digest	Interprets what happened, what is clear, what is shaky, and what feedback means.
fgeo-growth	Turns goals or gaps into small practice, review, and the next better attempt.
fgeo-content	Turns learning, evidence, cases, and methods into platform-native expression.
fgeo-publish	Publishes tracked content and routes real-world signals back into learning.
fgeo-profile	Preserves identity, preferences, audience, boundaries, and long-term context.

Why This Matters

Most AI tools help with a single task:

summarize this
write this
publish this
fix this

fgeo tries to help with continuous growth:

What did I do?
What feedback appeared?
What did I not notice?
What should I absorb?
What should change next time?

The feedback loop is not a closing CTA. It is the core learning mechanism. fgeo should help the user notice signals they missed, absorb those signals, and turn them into the next action.

CLI Quick Start

Install and initialize fgeo:

pip install fgeo

fgeo init
fgeo enable codex

Create a project and publishing workflow:

fgeo project create fcontext --desc "AI context manager"
fgeo goal add fcontext "Help developers understand fcontext"
fgeo platform add fcontext devto --directions "tutorial,architecture" --pace "2/month"
fgeo plan create fcontext cold-start --strategy "Developer community launch"
fgeo plan assign fcontext cold-start devto --direction tutorial --target 3

Register content:

fgeo content register docs/first-post.md \
  --project fcontext \
  --platform devto \
  --plan cold-start \
  --direction tutorial \
  --type article \
  --status draft

Check status:

fgeo status fcontext

What the CLI Manages

The CLI keeps stable local state for the agent skills:

Profile -> Digest -> Growth -> Content -> Publish -> Feedback

It can track projects, goals, plans, platforms, content assets, publish tasks, learning records, evidence, gaps, baselines, and feedback signals.

The important point is not the database. The important point is that the agent does not lose the learning loop between sessions.

Supported Agent Integrations

fgeo enable <agent> writes fgeo instructions into each agent's native location and also enables fcontext for the same workspace.

Supported agents:

copilot, claude, cursor, trae, qwen, kiro, opencode,
openclaw, zed, pi, antigravity, codex